Distributed Generation Allocation in Radial Distribution Systems Using Various Particle Swarm Optimization Techniques

• Autorzy: Farhadi P. , Ghadimi N. , Sojoudi T.

Warianty tytułu

PL

Optymalizacja alokacji system generacji rozproszonej w sieci radialnej z wykorzystaniem algorytmu PSO

• Języki publikacji: EN

Abstrakty

EN

Distributed generations (DGs) are continuously integrated into the distribution systems either by the utilities or the customers. Site and size of DGs have significant impacts on the system power losses . In this paper, the most recent and practical PSO algorithms are used to optimally allocate DGs in radial distribution systems, and the obtained results are discussed and compared to each other. The single objective is to minimize network power losses using the least possible injected power from DGs. To have a good benchmark for comparisons of different PSO Techniques, simulations carried out on IEEE 33-bus and 69-bus standard radial distribution systems.

PL

Rozproszone układy generacji DGs mogą być dołączane do system energetycznego albo przez wytwórcę albo użytkownika. W artykule przedstawiono algorytm PSO umożliwiający optymalizację dołączenia system DGs do sieci radialnej. Głównym celem optymalizacji jest zmniejszenie strat mocy.

Słowa kluczowe

EN

distributed generation; optimal allocation; particle swarm optimization (PSO); active power losses

PL

sieci energetyczne; algorytm PSO; generacja rozproszona

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1a
• Strony: 261--265
• Opis fizyczny: Bibliogr. 20 poz., schem., tab., wykr.

Twórcy

autor

Farhadi P.

• Young Researchers Club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran

autor

Ghadimi N.

• Young Researchers Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran

autor

Sojoudi T.

• Young Researchers Club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran

Bibliografia

• [1] Ackermann T, Andersson G, Söder L. Distributed generation: a definition. Electric Power System Researcg, 71 (2004),119– 128.
• [2] Qi Kang, TianLan, YongYan, LeiWanga, QidiWu. Group search optimizer based optimal location and capacity of distributed generations. Neurocomputing, 78 (2012), 55–63.
• [3] Keane, M. O’Malley, Optimal distributed generation plant mix with novel loss adjustment factors, in: IEEE Power Eng. Society General Meeting, (2006).
• [4] Y.A. Katsigiannis, P.S. Georgilakis, Optimal sizing of small isolated hybrid power systems using tabu search, Journal of Optoelectronics and Advanced Materials 10 (5) (2008) 1241– 1245.
• [5] Duong Quoc Hung, Nadarajah Mithulananthan, and R. C. Bansal, Analytical Expressions for DG Allocation in Primary Distribution Networks. IEEE Trans. on Energy Conv.,25 (2010).
• [6] Fahad S. Abu-Mouti, and M. E. El-Hawary, Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm, IEEE Transactions on Power Delivery. 26 (2011).
• [7] Favuzza S, Graditi G, Ippolito MG, Sanseverino ER. Optimal electrical distribution systems reinforcement planning using gas turbines by dynamic ant colony search algorithm. IEEE Transactions on Power System. 22 (2007), 580–587.
• [8] Taher Niknam, Seyed Iman Taheri, Jamshid Aghaei, Sajad Tabatabaei, Majid Nayeripour. A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Applied Energy 88 (2011), 4817– 4830.
• [9] Harrison G. P., Piccolo A., Siano P., Wallace A. R.: ‘Hybrid GA and OPF evaluation of network capacity for distribution generation connections’, Electr. Power Energy Syst., 2008, 78, pp. 392–398.
• [10] J. Kennedy, R.C. Eberhart, Particle swarm optimization, Proc. IEEE International Conference on Neural Networks (1995), 1942–1948.
• [11] Davoud Sedighizadeh and Ellips Masehian, Particle Swarm Optimization Methods, Taxonomy and Applications, International Journal of Computer Theory and Engineering, 5 (2009), 1793-8201.
• [12] Jen-Hao Teng. A direct approach for distribution system load flow solutions. IEEE Transactions on Power Delivery. 18 (2003).
• [13] M.H. Moradi, M. Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Electrical Power and Energy Systems 34 (2012), 66–74.
• [14] Shen, X., Wei, K., Wu, D., Tong, Y. and Li, Y.,A Dynamic Adaptive Dissipative Particle Swarm Optimization with Mutation Operation, Proc. IEEE/ ICCA, (2007), 586-589.
• [15] Wang, X.,Wang, Y., Zeng, H., and Zhou, H., Particle Swarm Optimization with Escape Velocity. Int. Conf. on Computational Intell. and Security,1 (2006). 457-460.
• [16] He, S., Wu, Q.H., Wen, J.Y., Saunders, J.R. and Paton, R.C.,A particle swarm optimizer with passive congregation, Journal of Biosystems, 78 (2004), 135-147.
• [17] Atyabi, A. and Phon-Amnuaisuk, S.,Particle swarm optimization with area extension (AEPSO), IEEE/CEC, (2007),1970-1976.
• [18] P. Farhadi, H. Shayeghi, T. Sojoudi and M. Karimi, Customer reliability Improvement and power loss reduction in distribution systems using distributed generations, Indian Journal of Science and Technology, 5 (2012), 2313-2317.
• [19] Baran ME, Wu FF. Optimal Sizing of capacitor placed on radial distribution systems. IEEE Transactions on Power Delivery 4(1989), 735–43.
• [20] Hamouda Abdellatif, Zehar Khaled. Efficient load flow method for radial distribution feeders. Journal of Applied Science. 13 (2006), 2741–2748.